xinntao / realesrgan

Practical Image Restoration Algorithms for General/Anime Images

Cold

Public
7M runs
T4
GitHub
Paper
License

Run with an API

Playground API Examples README Versions

Input

Run this model in Node.js with one line of code:

npx create-replicate --model=xinntao/realesrgan

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run xinntao/realesrgan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "xinntao/realesrgan:1b976a4d456ed9e4d1a846597b7614e79eadad3032e9124fa63859db0fd59b56",
  {
    input: {
      img: "https://replicate.delivery/mgxm/f50a356c-5dc9-411b-a93e-4e5bb487a2cc/0030.jpg",
      tile: 0,
      scale: 2,
      version: "Anime - anime6B",
      face_enhance: false
    }
  }
);
console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run xinntao/realesrgan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "xinntao/realesrgan:1b976a4d456ed9e4d1a846597b7614e79eadad3032e9124fa63859db0fd59b56",
    input={
        "img": "https://replicate.delivery/mgxm/f50a356c-5dc9-411b-a93e-4e5bb487a2cc/0030.jpg",
        "tile": 0,
        "scale": 2,
        "version": "Anime - anime6B",
        "face_enhance": False
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run xinntao/realesrgan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "1b976a4d456ed9e4d1a846597b7614e79eadad3032e9124fa63859db0fd59b56",
    "input": {
      "img": "https://replicate.delivery/mgxm/f50a356c-5dc9-411b-a93e-4e5bb487a2cc/0030.jpg",
      "tile": 0,
      "scale": 2,
      "version": "Anime - anime6B",
      "face_enhance": false
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/xinntao/realesrgan@sha256:1b976a4d456ed9e4d1a846597b7614e79eadad3032e9124fa63859db0fd59b56 \
  -i 'img="https://replicate.delivery/mgxm/f50a356c-5dc9-411b-a93e-4e5bb487a2cc/0030.jpg"' \
  -i 'tile=0' \
  -i 'scale=2' \
  -i 'version="Anime - anime6B"' \
  -i 'face_enhance=false'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/xinntao/realesrgan@sha256:1b976a4d456ed9e4d1a846597b7614e79eadad3032e9124fa63859db0fd59b56
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "img": "https://replicate.delivery/mgxm/f50a356c-5dc9-411b-a93e-4e5bb487a2cc/0030.jpg",
      "tile": 0,
      "scale": 2,
      "version": "Anime - anime6B",
      "face_enhance": false
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

Generated in

4.3 seconds

Tweak itReport

Examples

View more examples

Run time and cost

This model costs approximately $0.0022 to run on Replicate, or 454 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 10 seconds.

Readme

Real-ESRGAN

Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.

If Real-ESRGAN is helpful, please help to ⭐ the Github Repo and recommend it to your friends 😊

Model Descriptions

Option	Weight Name	Description
▶General - RealESRGANplus:	<RealESRGAN_x4plus.pth>	A large model for general images
▶General - v3:	<realesr-general-x4v3.pth>	A tiny model for general images
▶Anime - anime6B:	<RealESRGAN_x4plus_anime_6B.pth>	A large model for anime images/illustrations
▶AnimeVideo - v3:	<realesr-animevideov3.pth>	A tiny model for anime images, especially for anime videos

📧Contact

If you have any question, please email xintao.wang@outlook.com or xintaowang@tencent.com.